Nova Southeastern University
H. Wayne Huizenga School of Business & Entrepreneurship Assignment for Course: | QNP 5000 | Submitted to: | | Submitted by: | | | | | | |
Date of Submission
Title of Assignment: Case 5.3 “Boise Cascade Corporation

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Introduction:
A mill manager from the Boise Cascade Corporation would like to calculate the number of scaling stations that should be in operation at any given time. The manager wants to minimize the time a truck has to wait, at the same the manager doesn’t want to have scales open unnecessarily, wasting workers time and the company money. Having to many scales open without enough trucks will result in needlessly high cost and wasteful spending, while not having enough scales open will result in trucks having to wait. The manager has to decide the optimal number of scales to open so that the Boise Cascade Corporation runs effectively and efficiently. Boise Cascade Corporation receives lumber mill logs, scales them, and puts them in a log pond. Each scale can operate at a capacity of 6 trucks per hour. The manager has studied the pattern and came up with the conclusion that between 7:00 A.M.-8:00 A.M. on average 12 trucks arrive randomly per hour. If the mill manager wants to determine how many scales, they should have open in any given hour they should use Poisson Probability Distribution. Poisson distribution is used for events occurring randomly in any given time, because our data is discrete and we our searching for the probabilities of the number of trucks arriving at any given hour relative to how many scales would need to be open, we used Poisson Probability Distribution. Using the Poisson Probability Distribution, we can suggest to the mill manager to open 3 scales. While operating 3 scales the manager would be covered for 96.27% of all possibilities, with less than a 4% chance a truck would have to wait. Operating at 2 significantly increases the chances a truck would wait.
Analysis:
Poisson’s distribution was used to analyze the probability of 6, 12 and 18 trucks arriving in a given hour. The capacity of the weigh station is 3 lanes. Each lane has a capacity of 6 trucks per hour. The capacity of the operation using all lanes is 18 trucks per hour. Figure 1 is the Poisson distribution given a mean of 12 trucks. Table 1 summarizes the probability of exactly 0- 20 trucks arriving in an hour. Table 2 is the probability of 0-6 trucks, 7-12 trucks and 13-18 trucks arriving in an hour and the probability of 1 customer waiting using the complement rule. Table 3 is a sum
Chart 1

Table 1 Number of trucks per hour | Chance of Occurrence | 0 | 6.14421E-06 | 1 | 7.37305E-05 | 2 | 0.000442383 | 3 | 0.001769533 | 4 | 0.005308599 | 5 | 0.012740639 | 6 | 0.025481277 | 7 | 0.04368219 | 8 | 0.065523285 | 9 | 0.08736438 | 10 | 0.104837256 | 11 | 0.114367916 | 12 | 0.114367916 | 13 | 0.105570384 | 14 | 0.0904889 | 15 | 0.07239112 | 16 | 0.05429334 | 17 | 0.038324711 | 18 | 0.025549807 | 19 | 0.01613672 | 20 | 0.009682032 | Table 2 Lane Capacity | Number of trucks per hour | Cumulative | Chance of Waiting | 1 lane open | 6 | 4.58% | 95% | 2 lanes open | 12 | 57.60% | 42% | 3 lanes open | 18 | 96.26% | 4% |

Table 3